TRAINING_PARAMS = \
{
    "model_params": {
        "backbone_name": "darknet_53",
        "backbone_pretrained": "",
    },
    "yolo": {
        # 原始数据集的anchors
        # "anchors": [[[12, 15], [12, 13], [14, 17]],
        #             [[10, 16], [11, 13], [12, 18]],
        #             [[9, 10], [10, 14], [10, 12]]],
        # "classes": 1,
        # oblique_U_hoop_nut_06
        # "anchors": [[[14, 16], [15, 17], [16, 21]],
        #             [[13, 17], [13, 13], [14, 19]],
        #             [[10, 13], [11, 17], [11, 15]]],
        # "classes": 80,
        # oblique_U_hoop_nut_23
        # "anchors": [[[12, 14], [13, 17], [15, 21]],
        #             [[11, 16], [12, 21], [12, 19]],
        #             [[10, 14], [10, 20], [10, 17]]],
        # "classes": 80,
        # flat_U_hoop_nut_22
        # "anchors": [[[13, 21], [14, 18], [15, 22]],
        #             [[11, 19], [11, 13], [13, 15]],
        #             [[9, 12], [10, 14], [11, 16]]],
        # "classes": 80,
        # flat_U_hoop_nut_05
        "anchors": [[[12, 15], [13, 18], [14, 15]],
                    [[11, 17], [11, 14], [12, 12]],
                    [[9, 10], [10, 15], [10, 12]]],
        "classes": 80,
    },

    "batch_size": 16,
    "iou_thres": 0.75,
    "annotation_path": "../data/coco/annotations/instances_val2014.json",
    "img_h": 416,
    "img_w": 416,
    "parallels": [0],
    # 原先的数据集地址
    # "val_path": "../val_image_path.txt",
    # "val_label_path": "../val_label_path.txt",
    # "pretrain_snapshot": "../test/model.pth",     # 原先的数据集训练的模型

    # 现在的数据集地址oblique_U_hoop_nut_06  AP0.25->92.05%   AP0.45->90.38%
    # "val_path": "/mnt/sda1/cq/4c/oblique_U_hoop_nut_06/oblique_06_image_416_sorted_val.txt",
    # "val_label_path": "/mnt/sda1/cq/4c/oblique_U_hoop_nut_06/label_normalize/oblique_06_label_all_sorted_val.txt",
    # "pretrain_snapshot": "../bobliu_yolov3/darknet_53/size416x416_try4/20190305191209/oblique_06_model.pth",

    # 现在的数据集地址oblique_U_hoop_nut_23  AP0.5->76.3%   AP0.25->95.74%
    # "val_path": "/mnt/sda1/cq/4c/oblique_U_hoop_nut_23/oblique_23_image_416_sorted_val.txt",
    # "val_label_path": "/mnt/sda1/cq/4c/oblique_U_hoop_nut_23/label_normalize/oblique_23_label_all_sorted_val.txt",
    # "pretrain_snapshot": "../bobliu_yolov3/darknet_53/size416x416_try4/20190311182629/flat_22_model.pth",
    # AP0.5->77.25%   AP0.25->95.26%
    # "pretrain_snapshot": "./oblique_23_model_66.pth",

    # 现在的数据集地址flat_U_hoop_nut_22  AP0.25-> 96.69%   AP0.5-> 82.3%
    # "val_path": "/mnt/sda1/cq/4c/flat_U_hoop_nut_22/flat_22_image_416_sorted_val.txt",
    # "val_label_path": "/mnt/sda1/cq/4c/flat_U_hoop_nut_22/label_normalize/flat_22_label_all_sorted_val.txt",
    # "pretrain_snapshot": "../bobliu_yolov3/darknet_53/size416x416_try4/20190312114016/flat_22_model.pth",

    # 现在的数据集地址flat_U_hoop_nut_05  AP0.25-> 91.2%   AP0.5-> 70.0%
    "val_path": "/mnt/sda1/cq/4c/flat_U_hoop_nut_05/flat_05_image_416_sorted_val.txt",
    "val_label_path": "/mnt/sda1/cq/4c/flat_U_hoop_nut_05/label_normalize/flat_05_label_all_sorted_val.txt",
    "pretrain_snapshot": "../bobliu_yolov3/darknet_53/size416x416_try4/20190312160140/flat_05_model.pth",
    # AP0.5-> 70.0%   AP0.25-> 90.8%
    # "pretrain_snapshot": "./flat_05_model_101.pth",
}
